Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "29" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 37 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 35 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 1.08% | -0.907040 | -0.915425 | -0.599014 | -0.300694 | -1.634466 | -1.106308 | -1.248370 | -0.484712 | 0.8412 | 0.8399 | 0.2342 | 2.857123 | 2.780193 |
| 2459851 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 11.76% | 4.28% | -1.042309 | -0.687416 | -0.568275 | -0.383090 | -1.220098 | -0.597936 | -0.873984 | 0.831640 | 0.7734 | 0.7540 | 0.3208 | 1.957705 | 1.740431 |
| 2459850 | digital_ok | 0.00% | 0.00% | 0.00% | 0.58% | 18.02% | 0.00% | -1.472619 | -1.166606 | -0.828184 | -0.469056 | -1.172327 | -0.859490 | -0.448487 | 1.213943 | 0.7510 | 0.7555 | 0.3439 | 1.839424 | 1.577690 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 0.00% | -1.412521 | -1.382738 | -0.248886 | 0.595399 | -0.874043 | -1.130891 | -0.439635 | 0.410427 | 0.7510 | 0.7485 | 0.3515 | 1.536675 | 1.461955 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.50% | -1.341214 | -0.686680 | 0.366947 | 0.438232 | -0.483273 | -0.887610 | -0.458959 | 1.464079 | 0.7322 | 0.7518 | 0.3692 | 1.543125 | 1.416276 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.21% | 0.00% | -0.972473 | -0.697674 | 0.403197 | 0.497172 | -1.041920 | -1.083069 | -0.670803 | 0.686894 | 0.7332 | 0.6886 | 0.4228 | 1.608943 | 1.521978 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | -1.340245 | -1.112266 | -0.028908 | 0.517005 | -0.511786 | -0.662527 | -0.593781 | 1.396740 | 0.8587 | 0.6930 | 0.4706 | 1.699104 | 1.596733 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 97.79% | 2.21% | -0.690131 | -0.860710 | 0.802848 | 1.947864 | -0.998521 | -1.251909 | -0.821145 | -0.159938 | 0.7510 | 0.7619 | 0.3553 | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.142008 | -0.171291 | 2.183905 | 2.915808 | 1.160371 | -0.378829 | -0.000174 | -1.276890 | 0.0280 | 0.0251 | 0.0017 | nan | nan |
| 2459843 | digital_ok | 0.00% | 0.66% | 0.66% | 0.00% | 15.22% | 0.00% | -1.907807 | -0.817807 | -0.676839 | 1.348406 | -1.115627 | -0.507277 | -0.805076 | 1.034000 | 0.7568 | 0.7602 | 0.3716 | 1.915467 | 1.722328 |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.028568 | -0.063971 | -0.228702 | -0.684512 | 0.102470 | 1.092477 | -0.005085 | 1.407028 | 0.7566 | 0.6580 | 0.2710 | 1.971865 | 1.820471 |
| 2459841 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.669743 | 2.689893 | 0.611140 | 2.292778 | -0.326158 | 1.145428 | -0.032812 | -0.679071 | 0.0276 | 0.0253 | 0.0017 | nan | nan |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.139807 | 1.126279 | -1.946161 | -2.718100 | 0.570628 | -0.132843 | 0.130120 | -1.173133 | 0.0263 | 0.0239 | 0.0016 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | -0.041481 | 0.053239 | 0.493605 | 0.042784 | -1.092537 | -1.044751 | -1.584124 | -1.131519 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.148753 | -0.897771 | -0.237330 | -0.273401 | -0.880526 | -0.520182 | -0.510224 | 0.078979 | 0.7420 | 0.6785 | 0.4170 | 2.074913 | 2.031927 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0366 | 0.0414 | 0.0013 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.395066 | -0.465600 | 0.090351 | -1.067630 | 2.126154 | 1.520710 | 0.194102 | 0.000775 | 0.0357 | 0.0392 | 0.0005 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.302187 | -0.530946 | -1.092007 | -0.726958 | -0.087718 | 0.056241 | -0.496492 | -0.004112 | 0.0338 | 0.0333 | 0.0023 | nan | nan |
| 2459832 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.203120 | 0.267748 | 0.882457 | 0.254599 | -0.718225 | -1.208603 | -0.883601 | -0.820611 | 0.0417 | 0.0432 | 0.0036 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.446546 | -0.414019 | -0.017743 | -0.305130 | -0.494069 | -1.494881 | -0.099237 | 0.635420 | 0.8085 | 0.5080 | 0.5974 | 1.611008 | 1.304308 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 75.82% | 24.18% | -1.302913 | -1.946102 | -0.213190 | -0.145999 | -0.946912 | -1.381105 | 0.010411 | 0.226938 | 0.7470 | 0.6386 | 0.4364 | 1.107111 | 1.097755 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.121534 | -0.484507 | 0.000406 | -0.002064 | -0.852459 | -0.659971 | -0.503141 | 0.544269 | 0.8069 | 0.5280 | 0.5694 | 1.564083 | 1.511696 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.739991 | -0.925814 | 0.696019 | -0.347990 | -0.946302 | -0.630504 | -0.249709 | 0.353146 | 0.7559 | 0.6462 | 0.4379 | 1.181462 | 1.007180 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | -0.811766 | -0.257464 | 0.581237 | -0.531394 | -0.396553 | -0.127391 | -0.253233 | 0.043539 | 0.8037 | 0.5495 | 0.5430 | 1.588814 | 1.255100 |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.098846 | -0.044698 | -0.177894 | -0.259509 | 7.040850 | 7.142514 | 2.876601 | 3.396272 | 0.8028 | 0.5517 | 0.5457 | 7.504901 | 4.394251 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.586379 | -1.056569 | -0.192184 | -0.399327 | 2.939382 | 2.915744 | 8.162274 | 9.383372 | 0.6964 | 0.6806 | 0.4023 | 6.088248 | 9.884213 |
| 2459823 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 11.11% | -0.867705 | -0.326409 | 0.456486 | -0.327406 | -0.181231 | -1.447386 | -0.382522 | 2.986091 | 0.7603 | 0.6057 | 0.5002 | 3.896885 | 3.923334 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.177077 | -0.017193 | 0.400968 | -0.130594 | -1.079847 | -0.722094 | -0.322610 | -0.566018 | 0.8073 | 0.5870 | 0.5403 | 2.016715 | 1.639668 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.036302 | -0.029975 | 0.230015 | -0.401505 | -0.408663 | -0.037015 | -0.435555 | 0.661435 | 0.8057 | 0.6156 | 0.5199 | 2.302549 | 2.071164 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.07% | -1.046968 | -0.922258 | 0.361336 | -0.360965 | -2.092074 | -1.427568 | -0.497487 | -0.521167 | 0.7779 | 0.6819 | 0.4222 | 1.854157 | 1.864958 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.146542 | -0.077879 | 0.119103 | -0.321906 | -0.574420 | -0.806012 | 1.333512 | 1.419123 | 0.8253 | 0.6826 | 0.5063 | 2.143179 | 2.213275 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.354149 | 0.154455 | -0.033192 | -0.209772 | -0.721792 | 0.247160 | -0.546220 | 3.128319 | 0.8531 | 0.6007 | 0.5895 | 1.650284 | 1.496155 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.864405 | 0.276897 | -0.079984 | -0.533033 | -1.091927 | -0.711156 | -0.644913 | 0.924475 | 0.8153 | 0.6849 | 0.5157 | 2.329289 | 2.417322 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Power | -0.300694 | -0.907040 | -0.915425 | -0.599014 | -0.300694 | -1.634466 | -1.106308 | -1.248370 | -0.484712 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.831640 | -1.042309 | -0.687416 | -0.568275 | -0.383090 | -1.220098 | -0.597936 | -0.873984 | 0.831640 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 1.213943 | -1.472619 | -1.166606 | -0.828184 | -0.469056 | -1.172327 | -0.859490 | -0.448487 | 1.213943 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Power | 0.595399 | -1.412521 | -1.382738 | -0.248886 | 0.595399 | -0.874043 | -1.130891 | -0.439635 | 0.410427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 1.464079 | -0.686680 | -1.341214 | 0.438232 | 0.366947 | -0.887610 | -0.483273 | 1.464079 | -0.458959 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.686894 | -0.697674 | -0.972473 | 0.497172 | 0.403197 | -1.083069 | -1.041920 | 0.686894 | -0.670803 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 1.396740 | -1.340245 | -1.112266 | -0.028908 | 0.517005 | -0.511786 | -0.662527 | -0.593781 | 1.396740 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Power | 1.947864 | -0.860710 | -0.690131 | 1.947864 | 0.802848 | -1.251909 | -0.998521 | -0.159938 | -0.821145 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Power | 2.915808 | -0.142008 | -0.171291 | 2.183905 | 2.915808 | 1.160371 | -0.378829 | -0.000174 | -1.276890 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Power | 1.348406 | -0.817807 | -1.907807 | 1.348406 | -0.676839 | -0.507277 | -1.115627 | 1.034000 | -0.805076 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 1.407028 | -1.028568 | -0.063971 | -0.228702 | -0.684512 | 0.102470 | 1.092477 | -0.005085 | 1.407028 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 2.689893 | 0.669743 | 2.689893 | 0.611140 | 2.292778 | -0.326158 | 1.145428 | -0.032812 | -0.679071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 1.126279 | 0.139807 | 1.126279 | -1.946161 | -2.718100 | 0.570628 | -0.132843 | 0.130120 | -1.173133 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.493605 | 0.053239 | -0.041481 | 0.042784 | 0.493605 | -1.044751 | -1.092537 | -1.131519 | -1.584124 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.078979 | -0.897771 | -1.148753 | -0.273401 | -0.237330 | -0.520182 | -0.880526 | 0.078979 | -0.510224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Temporal Variability | 2.126154 | -0.465600 | 0.395066 | -1.067630 | 0.090351 | 1.520710 | 2.126154 | 0.000775 | 0.194102 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Variability | 0.056241 | -0.530946 | -0.302187 | -0.726958 | -1.092007 | 0.056241 | -0.087718 | -0.004112 | -0.496492 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.882457 | 0.203120 | 0.267748 | 0.882457 | 0.254599 | -0.718225 | -1.208603 | -0.883601 | -0.820611 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.635420 | -1.446546 | -0.414019 | -0.017743 | -0.305130 | -0.494069 | -1.494881 | -0.099237 | 0.635420 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.226938 | -1.946102 | -1.302913 | -0.145999 | -0.213190 | -1.381105 | -0.946912 | 0.226938 | 0.010411 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.544269 | -0.484507 | -1.121534 | -0.002064 | 0.000406 | -0.659971 | -0.852459 | 0.544269 | -0.503141 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.696019 | -0.739991 | -0.925814 | 0.696019 | -0.347990 | -0.946302 | -0.630504 | -0.249709 | 0.353146 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.581237 | -0.257464 | -0.811766 | -0.531394 | 0.581237 | -0.127391 | -0.396553 | 0.043539 | -0.253233 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Variability | 7.142514 | -0.044698 | -1.098846 | -0.259509 | -0.177894 | 7.142514 | 7.040850 | 3.396272 | 2.876601 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 9.383372 | -0.586379 | -1.056569 | -0.192184 | -0.399327 | 2.939382 | 2.915744 | 8.162274 | 9.383372 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 2.986091 | -0.326409 | -0.867705 | -0.327406 | 0.456486 | -1.447386 | -0.181231 | 2.986091 | -0.382522 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.400968 | -1.177077 | -0.017193 | 0.400968 | -0.130594 | -1.079847 | -0.722094 | -0.322610 | -0.566018 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.661435 | -0.029975 | -1.036302 | -0.401505 | 0.230015 | -0.037015 | -0.408663 | 0.661435 | -0.435555 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 0.361336 | -1.046968 | -0.922258 | 0.361336 | -0.360965 | -2.092074 | -1.427568 | -0.497487 | -0.521167 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 1.419123 | -1.146542 | -0.077879 | 0.119103 | -0.321906 | -0.574420 | -0.806012 | 1.333512 | 1.419123 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 3.128319 | 0.154455 | -0.354149 | -0.209772 | -0.033192 | 0.247160 | -0.721792 | 3.128319 | -0.546220 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Temporal Discontinuties | 0.924475 | 0.276897 | -0.864405 | -0.533033 | -0.079984 | -0.711156 | -1.091927 | 0.924475 | -0.644913 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |